Learning the distribution of object trajectories for event recognition
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Learning variable-length Markov models of behavior
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A state-based technique for the summarization and recognition of gesture
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Dining Activity Analysis Using a Hidden Markov Model
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Unsupervised language learning for discovered visual concepts
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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The area of unsupervised activity categorization in computer vision is much less explored compared to the general practice of supervised learning of activity patterns. Recent works in the lines of activity "discovery" have proposed the use of probabilistic suffix trees (PST) and its variants which learn the activity models from temporally ordered sequences of object states. Such sequences often contain lots of objectstate self-transitions resulting in a large number of PST nodes in the learned activity models. We propose an alternative method of mining these sequences by avoiding to learn the self-transitions while maintaining the useful statistical properties of the sequences thereby forming a "compressed suffix tree" (CST). We show that, on arbitrary sequences with significant self-transitions, the CST achieves a much lesser size as compared to the polynomial growth of the PST. We further propose a distance metric between the CSTs using which, the learned activity models are categorized using hierarchical agglomerative clustering. CSTs learned from object trajectories extracted from two data sets are clustered for experimental verification of activity discovery.